{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/use-of-machine-learning-algorithms-for","title":"Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data","arxiv_id":null,"date":"2019-10-11","proceeding":"International Journal of Applied and Basic Medical Research 2019 10","authors":["Zahra Hoodbhoy","Mohammad  Noman","Ayesha Shafique","Ali Nasim","Devyani  Chowdhury","Babar Hasan"],"abstract":"Background: A major contributor to under‑five mortality is the death of children in the 1st month \r\nof life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal \r\ncardiotocograph (CTGs) can be used as a monitoring tool to identify high‑risk women during labor. \r\nAim: The objective of this study was to study the precision of machine learning algorithm techniques \r\non CTG data in identifying high‑risk fetuses. Methods: CTG data of 2126 pregnant women were \r\nobtained from the University of California Irvine Machine Learning Repository. Ten different \r\nmachine learning classification models were trained using CTG data. Sensitivity, precision, and F1 \r\nscore for each class and overall accuracy of each model were obtained to predict normal, suspect, \r\nand pathological fetal states. Model with best performance on specified metrics was then identified. \r\nResults: Determined by obstetricians’ interpretation of CTGs as gold standard, 70% of them were \r\nnormal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification \r\nmodels generated by XGBoost, decision tree, and random forest had high precision (>96%) to \r\npredict the suspect and pathological state of the fetus based on the CTG tracings. However, on \r\ntesting data, XGBoost model had the highest precision to predict a pathological fetal state (>92%). \r\nConclusion: The classification model developed using XGBoost technique had the highest prediction \r\naccuracy for an adverse fetal outcome. Lay health‑care workers in low‑ and middle‑income \r\ncountries can use this model to triage pregnant women in remote areas for early referral and further \r\nmanagement","url_abs":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822315/","url_pdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822315/pdf/IJABMR-9-226.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"use-of-machine-learning-algorithms-for","repo_url":"https://github.com/dubeyakshat07/Fetal-state-classification-using-cardiotocography-data-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}